To be of any strategic use, large data sets must be analyzed and visualized by forming and asking key questions and then by organizing the data to answer those questions. Analytics provide meaningful patterns in the data, and data visualization communicates the information clearly through graphical means. This course is designed to familiarize students with core concepts in communicating information through effective data visualization. The course introduces students to the elements of data visualization and elementary graphics programming, beginning with two-dimensional vector graphics and the programming platforms for graphics, moving into the design and construction of visualizations incorporating animation and user interactivity. Students will gain experience with hierarchical layouts and networks, the visualization of database and data mining processes, methods specifically focused on visualization of unstructured information, such as text, and systems for visual analytics that provide strategic decision support.
Upon completion of the course, students will be able to:
Weekly Discussion Posts — Most weeks there will be a discussion board that addresses a topic within the current module. For each discussion board, you must submit an original post and respond to at least 2 posts from your colleagues. Your original post should be at least 350 words in length. Be substantive and clear. Most of your post should be your own words and ideas; use examples from class materials and/or peer reviewed articles to support your ideas. References should be cited using APA Style.
Weekly Assignments — In weeks 1-4, 6 and 8 there are a number of short assignments. You will use either Tableau, Excel, or Gephi to create these assignments. Please see Blackboard for specifics.
Explanatory Visualizations – In week 5 you will use what you have learned in the first 4 weeks to create an explanatory visualization presentation to compare physician performance on a number of quality measures.
Exploratory Visualization – In week 7 you will create an interactive dashboard in Tableau.
Week | Topic | Activities and Assignments | Dates |
1 | What is Data Visualization? |
Readings, Videos, & Tableau Tutorials as assigned Discussion Question Multiple Short Assignments |
10/25/2017 – 11/1/2017 |
2 | Understanding briefs and creating simple charts |
Readings & Videos as assigned Discussion Question Multiple Short Assignments |
11/1/2017 – 11/8/2017 |
3 | How do we know the data is good? |
Readings & Videos as assigned Discussion Question Multiple Short Assignments |
11/8/2017 – 11/15/2017 |
4 | Making the data usable |
Readings as assigned Discussion Question Multiple Short Assignments |
11/15/2017 – 11/22/2017 |
5 | Explanatory Visualizations |
Readings & Videos as assigned Discussion Question Explanatory Visualization (Presentation) |
11/22/2017 – 11/29/2017 |
6 | Principles of Good Visualizations |
Readings as Assigned Discussion Question Dashboard Assignment |
11/29/2017 – 12/6/2017 |
7 | Exploratory Dashboards |
Readings as assigned Discussion Question Exploratory Visualization (Interactive Dashboard) |
12/6/2017 – 12/13/2017 |
8 | Network Mapping |
Video Discussion Question Network Mapping Assignment |
12/13/2017 – 12/17/2017 |
Your grade in this course will be determined by the following criteria:
Assessment Item | Possible Points | Percent of Total Grade |
---|---|---|
Discussion Forums (6) | 18 pts - (3 pts each) | 18% |
Weekly Assignments (weeks 1-4, 6) | 42 points (point values vary by week) | 42% |
Explanatory Visualization (week 5) | 18 points | 18% |
Dashboard Project (week 7) | 18 points | 18% |
Network Map (week 8) | 4 points | 4% |
Total | 100 pts | 100% |
Grade | Points Grade | Point Average (GPA) |
A | 94 – 100% | 4.00 |
A- | 90 – 93% | 3.75 |
B+ | 87 – 89% | 3.50 |
B | 84 – 86% | 3.00 |
B- | 80 – 83% | 2.75 |
C+ | 77 – 79% | 2.50 |
C | 74 – 76% | 2.00 |
C- | 70 – 73% | 1.75 |
D | 64 – 69% | 1.00 |
F | 00 – 63% | 0.00 |
Theme: What is Data Visualization?
Learning Outcomes:
Readings:
Tableau Tutorials:
Tableau Help Page (to be used for reference, as needed):
Week 1 Discussion:
In this discussion you will critique a visualization. Please select one of the visualizations on this document: Marlaria visualizations; (all the visualizations were created based on recent data on malaria). To make sure that each of you picks a different visualization, put your name on the google doc next to the visualization you have chosen. Make sure you put your name on the document before you start working on your post. Please don’t erase anyone’s name who has already chosen a visualization.
Your critique should include answers to the following:
Week 1 Assignments:
You will set up several tools that you will use throughout the course. See Blackboard for details.
Theme: Understanding briefs and creating simple charts
Learning Outcomes:
Readings:
Videos:
Week 2 Discussion: Rethinking Visualizations
How have this week’s readings & viewings changed how you see data visualization and the process of creating visualizations?
Think of a past visualization that you’ve created and write how you would approach that differently now, whether that be in the process of assembling your brief/use case (or writing one because you didn’t have one), the design process, and/or the implementation.
If you haven’t done visualizations, then think of text that you’ve written that could have been visualized. How would you approach the task of creating the visualization(s)?
Week 2 Assignments:
In this week’s assignments you will create visualizations for cardiac quality measures. Note that you will create the visualizations in both Tableau AND Excel. See Blackboard for assignment specifics. Refer to Chapters 3, 4, and 7 in your text, The Best Boring Book Ever of Tableau for Healthcare, for assistance with creating text tables, line graphs, and bar charts.
Theme: How do we know the data is good?
Learning Outcomes:
Readings:
Videos:
Week 3 Discussion:
Healthcare data is not uniform across systems. For example, dates might use a mm/dd/yy format (numbers) or perhaps month name, date, year (words). Systems may also differ in measure, such as measuring a baby’s weight in ounces or grams. Inconsistent data needs to be cleaned up before it can be used to create data visualizations. Cleaning up that data takes time and is therefore costly. Read this article on how NPR ran into a problem with inconsistent data and what they did to clean it up: How We Cleaned Up And Ranked Our Listeners’ Favorite Albums of 2016
Think about another case where inconsistent data caused problems. Your example can be from your work life, your personal life, or something you read about in the media. If you can’t find an example, please imagine one. Describe the scenario. What was the process for ensuring that the data was consistent and cleaning up any inconsistent data? (or what should it have been if it wasn’t done) Were there lasting ramifications caused by the inconsistent data?
Week 3 Assignments:
In this week’s assignments you will use the HbA1c dataset and Tableau to create scatter plots, strip plots, and box plots to allow you to visualize different aspects of the data. See Blackboard for details. Refer to Chapters 13 and 14 in your text, The Best Boring Book Ever of Tableau for Healthcare, for assistance in creating scatter plots and box plots.
Theme: Making the Data Usable
Learning Outcomes:
Readings:
Presentation:
Week 4 Discussion: Levels of specificity
Data can be viewed at different levels of specificity. Let’s use an electronic calendar as an example. You can look at your calendar by day, by week, by month, and even by year. The calendar view you select, is influenced by the information you need.
Now consider a patient with heart failure who needs to lose weight and who also needs to track their weight on a daily basis because extreme changes in weight may indicate a change in disease status. What is the value of looking at this person’s weight change by day, week, month, year?
What is another example of health data that we might want to view at different levels of specificity? Why? If you don’t have experience looking at clinical data in this way, write about something that you might track: finances, weight loss, strength training, sport training, etc.
Week 4 Assignments:
You will use the HbA1c dataset and Tableau to create pie charts, a stacked bar chart, and a tree map. You will also be given a database with inconsistent data that you will have to clean up. In addition, you will join two datasets using Tableau. See Blackboard for details. Refer to your text, The Best Boring Book Ever of Tableau for Healthcare, pages 58, 62-64 for assistance with stacked bar charts and Chapter 26, pages 375-380 on joining datasets.
Theme: Explanatory Visualizations
Learning Outcomes:
Readings:
Videos:
Week 5 Discussion:
The week 5 discussion is ungraded. It is a peer support discussion. Use it to reach out to your classmates for assistance with the week 5 assignment. You should also plan to check it throughout the week to see if you can help others with challenges they may have.
Week 5 Assignment:
Explanatory Visualization – The week 5 assignment is the first of two key assessments in the course. The student will create an explanatory visualization to show physician and practice level performance compared to target values for 5 quality measures over time. Please see Blackboard for specifics.
Theme: Principles of Good Visualization
Learning Outcomes:
Readings:
Videos:
Visualization Upload:
Please upload the document or powerpoint that contains the visualization that you created last week to this Google Drive folder. You will need to look at one another’s visualizations to do in this week’s discussion post. Watch this short video, if you need help in uploading your visualization.
Week 6 Discussion:
What works in a visualization is often a matter of personal preference. Take a look at the visualizations that your classmates created last week. What elements of the visualizations were most effective for you? What elements did you find confusing? What elements were not as effective for you?
The purpose of this discussion is not to discuss individual visualizations, but rather to consider the visualizations as a whole and talk about elements of the visualizations: choices in data representation, color, composition, annotation, etc. Remember, you can find the visualizations in this Google Folder (where you should have put your own visualization at the beginning of this week).
Week 6 Assignment:
During the next two weeks, you will create exploratory visualizations. Exploratory visualizations are often referred to as interactive visualizations because the user can interact with the visualization to see the data from a variety of different views. The assignment for this week is to create an interactive dashboard using the HbA1c dataset that you have used in prior weeks. (Next week you will create an interactive dashboard from a dataset that is less familiar to you.) See Blackboard for explicit instructions. Refer to Chapter 20 in your text, The Best Boring Book Ever of Tableau for Healthcare, for assistance in creating a dashboard.
Theme: Exploratory Dashboards
Learning Outcomes:
Readings:
Review parts of the Kirk textbook to help you with your dashboard design, including:
Week 7 Discussion:
The week 7 discussion is ungraded. It is a peer support discussion. Use it to reach out to your classmates for assistance with the week 7 assignment. You should also plan to check it throughout the week to see if you can help others with challenges they may have.
Week 7 Assignment:
You will create an interactive dashboard based on a dataset that is new to you. You may choose how you wish to visualize the data, but users of your dashboard must be able to find answers to specific questions as they use your dashboard. Use this assignment to demonstrate what you have learned in this course. Refer to Chapter 20 in your text, The Best Boring Book Ever of Tableau for Healthcare, for assistance in creating a dashboard.
Theme: Network Mapping
Learning Outcomes:
Video:
Week 8 Discussion:
Discuss your thoughts on Nicholas Christakis’s Ted talk. He mentions a number of things, such as flu epidemics, that we can use social networking to predict. If you could have access to all the necessary data, what would you like to use social networking to predict? Why?
Week 8 Assignment:
You will use Gephi to create a network map that shows the relationships between HIV users and their contacts/partners. See Blackboard for specific instructions.
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